




"""simple docstring"""




import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				# TODO: is there an appropriate internal test set?
				__UpperCamelCase         =		"ssube/stable-diffusion-x4-upscaler-onnx"





				def   _lowerCAmelCase							(			self						,					_a=0					):





							"""simple docstring"""
							lowerCamelCase      =   floats_tensor((1, 3, 128, 128)						,					rng=random.Random(_a					)					)
							lowerCamelCase      =   torch.manual_seed(_a					)
							lowerCamelCase      =   {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,					provider="""CPUExecutionProvider"""					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs()
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1].flatten()

							# started as 128, should now be 512
							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]					)
							assert np.abs(image_slice - expected_slice					).max() < 1e-1





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,					provider="""CPUExecutionProvider"""					)
							lowerCamelCase      =   PNDMScheduler.from_config(pipe.scheduler.config						,					skip_prk_steps=_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs()
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]					)
							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,					provider="""CPUExecutionProvider"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs()
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,					provider="""CPUExecutionProvider"""					)
							lowerCamelCase      =   EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs()
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]					)
							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,					provider="""CPUExecutionProvider"""					)
							lowerCamelCase      =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs()
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1






@nightly
@require_onnxruntime
@require_torch_gpu
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   ort.SessionOptions()
							lowerCamelCase      =   False
							return options





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg"""					)
							lowerCamelCase      =   init_image.resize((128, 128)					)
							# using the PNDM scheduler by default
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    """ssube/stable-diffusion-x4-upscaler-onnx"""						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   """A fantasy landscape, trending on artstation"""

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(
							    prompt=_a						,					image=_a						,					guidance_scale=7.5						,					num_inference_steps=10						,					generator=_a						,					output_type="""np"""						,					)
							lowerCamelCase      =   output.images
							lowerCamelCase      =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]					)
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg"""					)
							lowerCamelCase      =   init_image.resize((128, 128)					)
							lowerCamelCase      =   LMSDiscreteScheduler.from_pretrained(
							    """ssube/stable-diffusion-x4-upscaler-onnx"""						,					subfolder="""scheduler"""					)
							lowerCamelCase      =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    """ssube/stable-diffusion-x4-upscaler-onnx"""						,					scheduler=_a						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   """A fantasy landscape, trending on artstation"""

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(
							    prompt=_a						,					image=_a						,					guidance_scale=7.5						,					num_inference_steps=20						,					generator=_a						,					output_type="""np"""						,					)
							lowerCamelCase      =   output.images
							lowerCamelCase      =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							lowerCamelCase      =   np.array(
							    [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]					)
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2









"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



